The quantitative biology core (QBC) will be lead by Drs. Nicholas Schork and Ali Torkamani of The Scripps Research Institute. The long-term objective of the QBC will be to provide the program project group with the computational expertise required for the generation, analysis, integration, and interpretation of whole genome kidney transplantation biomarker discovery assays. These biomarkers will serve as indicators of the mechanisms underiying transplantation immunity, and ultimately as predictors of transplant outcome. The QBC will complement the bioinformatics and biostatistics functions outlined in Core C in areas requiring special expertise in next generation deep sequencing, statistical genetics and functional and pathway analysis through the following specific aims: 1) Devise and implement the quantitative biology support for gene selections, primer designs, and data analysis of epigenetics profiling by RalnDance microdroplet PCR and deep DNA sequencing in parallel to genome-wide gene expression profiling, 2) Apply systems biology tools for functional pathway mapping and the impact of different immunosuppression drugs on these pathways in purified, in vitro-activated CD4 naive and memory T cells and B cells from normal donors and then patients with acute and chronic rejection, and 3) Support the efforts of Core C In the analysis and integration of data developed with target gene, pathway-centered genetics and whole exome sequencing. A computational primer design and algorithm specialized for design against bisulfite converted DNA will be generated to accomplish the goals of specific aim 1. A complementary sequence analysis pipeline for bisulfite converted next generation sequencing reads will be produced for the analysis and interpretation of the resultant data. Specific aim 2 will be executed through the implementation of pathway analysis and gene co-expression network reconstruction algorithms. Finally, to accomplish the goals stated in specific aim 3, genetic variant data will be generated using existing sequence analysis tools and analyzed using novel computational techniques for rare variant analysis, including set-based regression techniques, and functional annotation of variant impacts.